Gas Exchange of Carrot Leaves in Response to Elevated CO<sub>2</sub> Concentration
Bibliographic record
Abstract
Short-term responses of four carrot (Daucus carota) cultivars: Cascade, Caro Choice (CC), Oranza, and Red Core Chantenay (RCC) to CO2 concentrations (Ca) were studied in a controlled environment. Leaf net photosynthetic rate (PN), intercellular CO2 (Ci), stomatal conductance (gs), and transpiration rate (E) were measured at Ca from 50 to 1 050 μmol mol-1. The cultivars responded similarly to Ca and did not differ in all the variables measured. The PN increased with Ca until saturation at 650 μmol mol-1 (Ci= 350-400 μmol mol-1), thereafter PN increased slightly. On average, increasing Ca from 350 to 650 and from 350 to 1 050 μmol mol-1 increased PN by 43 and 52 %, respectively. The PNvs.Ci curves were fitted to a non-rectangular hyperbola model. The cultivars did not differ in the parameters estimated from the model. Carboxylation efficiencies ranged from 68 to 91 μmol m-2 s-1 and maximum PN were 15.50, 13.52, 13.31, and 14.96 μmol m-2 s-1 for Cascade, CC, Oranza, and RCC, respectively. Dark respiration rate varied from 2.80 μmol m-2 s-1 for Oranza to 3.96 μmol m-2 s-1 for Cascade and the CO2 compensation concentration was between 42 and 46 μmol mol-1. The gs and E increased to a peak at Ca= 350 μmol mol-1 and then decreased by 17 and 15 %, respectively when Ca was increased to 650 μmol mol-1. An increase from 350 to 1 050 μmol mol-1 reduced gs and E by 53 and 47 %, respectively. Changes in gs and PN maintained the Ci:Ca ratio. The water use efficiency increased linearly with Ca due to increases in PN in addition to the decline in E at high Ca. Hence CO2 enrichment increases PN and decreases gs, and can improve carrot productivity and water conservation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".